[Retracted] Automated Detection of Rehabilitation Exercise by Stroke Patients Using 3-Layer CNN-LSTM Model

Table 1

Comparison of 3-layer CNN-LSTM model with other standard models.

S. No	Other standard models	Proposed CNN-LSTM model
1	RNN-LSTM model used in [33] for HAR system and achieved great accuracy, but this model exaggerates the temporal and understates the spatial information as both of the models best fit for temporal data.	CNN is used for feature extraction and selection of useful features, while LSTM is used for exercise recognition. This model maintains a balance between spatial and temporal information.
2	In [40], LSTM-CNN model is used for activity recognition. The LSTM is used before CNN to process input data which is not efficient for the processing of spatial input data.	In the proposed model, 3-layer CNN is applied first to process spatial input data. The data is then fed to the LSTM layer to further refine the extracted data and detect the rehabilitation exercise.
3	The CNN model for exercise recognition was tested and observed that CNN learn too many complex parameters of about 2,575,753 during training.	The model learned about 392,765 parameters which conclude that CNN-LSTM model is lightweight which has reduced complexity and achieved better accuracy.
4	In [30], KNN is applied to recognize human activity which fails to address occlusion, deformation, and viewpoint variation, as KNN is using hand-crafted techniques.	The 3-layer CNN-LSTM model learns activity feature automatically and handles these issues efficiently. We used an RGB camera instead of Kinect sensors to reduce complexity and processing time.